Inequalities in healthcare use during the COVID-19 pandemic

The COVID-19 pandemic led to reductions in non-COVID related healthcare use, but little is known whether this burden is shared equally. This study investigates whether reductions in administered care disproportionately affected certain sociodemographic strata, in particular marginalised groups. Using detailed medical claims data from the Dutch universal health care system and rich full population registry data, we predict expected healthcare use based on pre-pandemic trends (2017 – Feb 2020) and compare these expectations with observed healthcare use in 2020 and 2021. Our findings reveal a 10% decline in the number of weekly treated patients in 2020 and a 3% decline in 2021 relative to prior years. These declines are unequally distributed and are more pronounced for individuals below the poverty line, females, older people, and individuals with a migrant background, particularly during the initial wave of COVID-19 hospitalisations and for middle and low urgency procedures. While reductions in non-COVID related healthcare decreased following the initial shock of the pandemic, inequalities persist throughout 2020 and 2021. Our results demonstrate that the pandemic has not only had an unequal toll in terms of the direct health burden of the pandemic, but has also had a differential impact on the use of non-COVID healthcare.

counts by age group have been rounded to multiples of five given small sample sizes.As a result, the total count across some demographic variables is higher than the overall total reported in the first row.Total (N) 13,677,385 13,796,082 13,925,588 14,071,420 14,164,191 Gender

Figure SI- 1 :
Figure SI-1: Regression coefficients showing additional declines in weekly non-COVID patients for demographic subgroups relative to a reference group.Each coefficient is scaled by that group's pre-pandemic weekly average.Linear regression is performed on 32 weekly timeseries for the period 2017-2021 reflecting each unique fully interacted demographic group.We include week, year and holiday controls, demographic covariates and random effects at the group level.Depicted coefficients show the interactions between each demographic variable and a single pandemic dummy ('Overall') as well as dummies for each individual COVID wave (see Methods).Error bars around estimates indicate standard errors.

Figure SI- 2a :
Figure SI-2a: Difference between the observed and predicted number of treated individuals per week in 2020, for oncological care.Colours differentiate between urgency types (high, middle, low, and no urgency).COVID hospital waves are depicted in shaded grey.

Figure SI- 2b :
Figure SI-2b: Difference between the observed and predicted number of treated individuals per week in 2020, for trauma care.Colours differentiate between urgency types (high, middle, low, and no urgency).COVID hospital waves are depicted in shaded grey.

Figure SI- 3a :
Figure SI-3a: Cumulative age-and sex-adjusted difference between the observed and predicted number of treated individuals in 2020 and 2020, across urgency types (rows) for all demographic groups (columns), for treatments related to oncology care.COVID hospital wavesCovid hospital waves are depicted in shaded grey.

Figure SI- 3b :
Figure SI-3b: Cumulative age-and sex-adjusted difference between the observed and predicted number of treated individuals in 2020 and 2020, across urgency types (rows) for all demographic groups (columns), for treatments related to trauma care.COVID hospital wavesCovid hospital waves are depicted in shaded grey.

Figure SI- 4a :
Figure SI-4a: Difference between the observed and predicted number of treated individuals per week in 2020 and 2021, when only including individuals receiving healthcare procedures involving outpatient visits, clinical activities, ER activities and diagnostics but excluding activities related to laboratory medicine.Colours differentiate between urgency types (high, middle, low, and no urgency).COVID hospital waves are depicted in shaded grey.

Figure SI- 4b :
Figure SI-4b: Cumulative age-and sex-adjusted difference between the observed and predicted number of treated individuals in 2020 and 2021 when only including individuals receiving healthcare procedures involving outpatient visits, clinical activities, ER activities and diagnostics but excluding activities related to laboratory medicine, across urgency types (rows) and demographic groups (columns).COVID hospital waves are depicted in shaded grey.

Figure SI- 5a :
Figure SI-5a: Difference between the observed and predicted number of treated individuals per week in 2020 and 2021, when only including individuals receiving healthcare procedures involving clinical and / or ER activities.Colours differentiate between urgency types (high, middle, low, and no urgency).COVID hospital wavesCovid hospital waves are depicted in shaded grey.Values depict three week moving averages.

Figure SI- 5b :
Figure SI-5b: Cumulative age-and sex-adjusted difference between the observed and predicted number of treated individuals in 2020 and 2021 when only including individuals receiving healthcare procedures involving clinical and / or ER activities, across urgency types (rows) and demographic groups (columns).Covid hospital waves are depicted in shaded grey.

Figure SI- 6a :
Figure SI-6a: Difference between the observed and predicted number of healthcare activities per week in 2020.Colours differentiate between urgency types (high, middle, low, and no urgency).COVID hospital waves are depicted in shaded grey.Values depict three week moving averages.

Figure SI- 6b :
Figure SI-6b: Cumulative age-and sex-adjusted difference between the observed and predicted number of activities in 2020 and 2021, across urgency types (rows) and demographic groups (columns).COVID hospital waves are depicted in shaded grey.

Figure SI- 7a :
Figure SI-7a: Difference between the observed and predicted number of treated individuals per week in 2019.Colours differentiate between urgency types (high, middle, low, and no urgency).Values depict three week moving averages.

Figure SI- 7b :
Figure SI-7b: Cumulative age-and sex-adjusted difference in observed versus expected healthcare users by urgency (rows) and demographic groups (columns) in 2019.

Figure SI- 8a :
Figure SI-8a: Difference between the observed and predicted number of treated individuals per week in 2020 and 2021 when using a negative binomial regression to make weekly predictions.Colours differentiate between urgency types (high, middle, low, and no urgency) and patients treated for COVID-19.COVID hospital waves are depicted in shaded grey.

Figure SI- 8b :
Figure SI-8b:Cumulative age-and sex-adjusted differences between the observed and predicted number of treated individuals in 2020 and 2021, across urgency types (rows) and demographic groups (columns) when using a negative binomial regression to make weekly predictions.COVID hospital waves are depicted in shaded grey.

Table SI - 2 :
Adult population composition The Netherlands between 2017 and 2020.The cutoff point of 120% of the social minimum is used to determine eligibility for benefits in The Netherlads.The social minimum further depends on the household composition, more accurately reflecting the needs of a household than an absolute cutoff. *

Table SI -3: Sociodemographic
variables included in the data.

Table SI -4: Total
count of individuals treated per week between 2017 and 2020 by urgency type.Note: An individual is counted as having received treatment in a given week at a given urgency level if they underwent at least one health activity with that urgency level that week.If an individual received both a high urgency and a low urgency treatment within a given week, they will appear in both urgency counts.

Table SI - 5 :
Total count of treatment activities between 2017 and 2020 and by demographic group

Table SI - 6 :
Total count of treatment activities between 2017 and 2020 and by urgency type An individual is counted as having received treatment in a given week if they underwent at least one health activity that week.If an individual received at least one health activity per week across two weeks, they will be counted twice.
Table SI-7: Average number of individuals treated per week between 2017 and 2020 (based on 52 weeks) and by demographic group.Note:

Table SI -8:
Counts of fully interacted demographic groups at the start of 2020 and 2021.

Table SI - 9 :
Descriptive statistics of the dataset used for multivariate analysis, containing 32 timeseries at the weekly level for the period 2017-2021.Week and year variables are omitted from the table for brevity.